Google Analytics: Mobile App Tracking with Firebase
Learn why app tracking matters, how Firebase app analytics differs from web tracking, and best practices for clean event structure, naming conventions, and data quality in GA4.
Ricardo Cristofolini
Senior Implementation Specialist, Data Solutions
I’m passionate about what I do. If you meet my manager or co-workers, they would say I’m a team player, engaged and always excited to learn something new. Like everyone else I have some flaws. However I’m not afraid to work around those to bring the best in myself and for the company
App tracking is a critical component of your digital analytics ecosystem. Despite its importance, it is often deprioritized or treated as secondary to web analytics tracking.
In reality, mobile and app usage now represent a dominant share of digital behavior, making accurate app tracking foundational rather than optional.
According to the report “DataReportal Digital 2025: Global Overview”, about 5.56 billion people are using the internet as of early 2025. Even further, DemandSage states that 95.9% of internet users worldwide access the internet through a mobile phone.
Further, many shoppers start purchases on mobile (58%) but switch to desktop on checkout. This means that approximately 58% of multi-device shoppers begin their journey on mobile. The same source states that mobile generates ~56% of e-commerce sales, compared to ~39% on desktop.
While much of this traffic may occur on mobile versions of websites (and therefore fall under web tracking), a significant portion of engagement and conversion activity happens inside mobile apps. According to this Adobe’s article, in recent years, mobile has steadily gained a share of online sales. For the 2024–2025 period, mobile online sales were projected to reach 53% during the holiday season.
It was also mentioned that mobile basket sizes tend to be smaller than desktop (“basket size on mobile is 32% smaller than on desktop”), highlighting meaningful differences in user behavior across devices.
Furthermore, the Global Device Share for eCommerce Purchases reported that for B2C e-commerce in 2025, 51.4% of purchase value is attributable to mobile devices, while 48.7% is attributed to desktop devices. This finding aligns with the Ecommerce Italia 2025 Report, which highlights a similar split: “in 2024 worldwide, mobile purchases (51.4%) surpassed desktop purchases (48.7%).”
Taken together, these statistics clearly illustrate the role mobile and in-app interactions play across modern customer journeys. When weighted against these trends, the importance that app tracking can play in your analytics is crucial. As a result, getting app tracking right is critical to understanding performance across channels and devices.
Multiple SDKs can be used to track mobile data, such as Amplitude, Mixpanel, or Segment. In this article, however, we will focus specifically on the Firebase SDK.
Firebase, Google’s comprehensive mobile development platform, offers powerful tools for collecting, analyzing, and acting upon app usage data for both Android and iOS applications. This article outlines best practices for implementing app tracking with Firebase, with a focus on data quality, organization, and long-term usability for analytics and business intelligence.
The Importance of Clean Data
The value of app tracking lies not just in collecting data, but in collecting clean, organized, and usable data. Poorly structured or inconsistent data can lead to inaccurate insights, flawed business decisions, and wasted resources.
Analytics teams, marketers, CEOs, managers, and data scientists all rely on a robust and reliable data foundation. That foundation supports everything from dashboards and reporting to pattern detection, behavioral analysis, and experience personalization.
There is something for everyone. However, the data must be good enough to be trusted and actionable.
Key Principles for Data Collection
High-quality app tracking starts with a clear set of data collection principles:
Accuracy: Ensure the data captured accurately reflects user actions and app states.
Completeness: Avoid missing data points that are critical for analysis.
Consistency: Maintain uniform naming conventions, data types, and structures across all events and properties.
Timeliness: Data should be collected and made available for analysis in a timely manner.
Relevance: Only collect data that directly contributes to answering specific business questions.
Event Structure: Best Practices
Everything starts somewhere.
If you are familiar with web-based tracking, you are most likely well-versed in data layers. When it comes to tracking data on websites, a well-formed and robust data layer can be critical to your tracking setup.
App tracking works differently. Whereas web analytics relies on a centralized dataLayer object that can persist information across multiple events on a page, Firebase app tracking treats each event as independent and transient.
With app tracking and the Firebase SDK, each event is triggered directly by the app using logEvent calls and sent immediately to the collection endpoint. There is no shared, persistent object that future events can reference.
This architectural difference makes event design, naming, and governance even more important in mobile app tracking.
Event Naming Conventions
Employ a clear and consistent naming convention for all events. This improves readability, governance, and downstream analysis.
Best practices include:
Lowercase with underscores: screen_view, product_added_to_cart
Action-object approach: view_item, add_to_cart, purchase
Prefixes for categories: ecommerce_purchase, engagement_session_start
Google provides extensive documentation on event naming conventions, including reserved event names and reserved prefixes. We strongly recommend reviewing this documentation, as the list is extensive and using reserved names can cause reporting issues.
If you already have a dataLayer present on your website, this is an excellent starting point for defining app event names. Do not try to reinvent the wheel. Using consistent naming across web and app helps ensure GA4 interprets events correctly.
For example, menu_interaction and menu_interaction_app would be treated as two distinct events in GA4, even if they represent the same user action in the same location.
Quick Tip: For this example, we recommend using the event name menu_interaction and relying on GA4’s default device dimensions to distinguish between web and app traffic.
Event Parameters
Each event should include relevant parameters that provide additional context. Parameters enable deeper analysis and more meaningful segmentation.
Standard parameters: Leverage Firebase’s recommended standard events and parameters, such as item_id, item_name, and value for e-commerce events.
Custom parameters: Define custom parameters for app-specific data points. Ensure they are consistently named and that their data types are clearly understood.
As with event names, avoid reinventing parameters when standard options already exist. Google provides multiple default parameters that can be reused across implementations. This reduces the need for custom definitions in GA4 and helps keep governance manageable, particularly for standard GA4 properties.
User Properties
User properties allow you to segment your audience and understand the characteristics of different user groups over time.
Examples include:
Demographic data: user_age_group, user_gender
App-specific traits: premium_user, subscription_status
Behavioral segments: last_app_version, days_since_last_purchase
When used consistently, user properties enable more meaningful audience analysis and long-term behavioral insights.
Building a Scalable Foundation for App Analytics
This is just the tip of the iceberg.
App tracking goes far beyond event names and parameters. However, getting these fundamentals right establishes the data foundation everything else depends on, from reporting and attribution to audience modeling and predictive analysis. Without clean, consistent app data, even the most advanced analytics strategies will fall short.
At Napkyn, we work with clients across industries to design, implement, and validate app tracking strategies using Firebase and GA4. In many cases, we see teams struggle not because they lack data, but because their app data is fragmented, inconsistent, or misaligned with how GA4 and downstream tools actually work. Addressing these issues early allows teams to move faster, trust their data, and unlock more advanced use cases over time.
Stay tuned for Part II, where we will cover common pitfalls in app tracking, how to make app data truly useful, how to define meaningful key performance indicators, and how to turn mobile data into actionable insights.
If you are reviewing your current app tracking setup or planning a new implementation, Napkyn can help you assess your data quality, align tracking with business goals, and ensure your Firebase and GA4 configuration is built to scale.
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Google Analytics: Mobile App Tracking with Firebase
Learn why app tracking matters, how Firebase app analytics differs from web tracking, and best practices for clean event structure, naming conventions, and data quality in GA4.
Ricardo Cristofolini
Senior Implementation Specialist, Data Solutions
January 21, 2026
I’m passionate about what I do. If you meet my manager or co-workers, they would say I’m a team player, engaged and always excited to learn something new. Like everyone else I have some flaws. However I’m not afraid to work around those to bring the best in myself and for the company
App tracking is a critical component of your digital analytics ecosystem. Despite its importance, it is often deprioritized or treated as secondary to web analytics tracking.
In reality, mobile and app usage now represent a dominant share of digital behavior, making accurate app tracking foundational rather than optional.
According to the report “DataReportal Digital 2025: Global Overview”, about 5.56 billion people are using the internet as of early 2025. Even further, DemandSage states that 95.9% of internet users worldwide access the internet through a mobile phone.
Further, many shoppers start purchases on mobile (58%) but switch to desktop on checkout. This means that approximately 58% of multi-device shoppers begin their journey on mobile. The same source states that mobile generates ~56% of e-commerce sales, compared to ~39% on desktop.
While much of this traffic may occur on mobile versions of websites (and therefore fall under web tracking), a significant portion of engagement and conversion activity happens inside mobile apps. According to this Adobe’s article, in recent years, mobile has steadily gained a share of online sales. For the 2024–2025 period, mobile online sales were projected to reach 53% during the holiday season.
It was also mentioned that mobile basket sizes tend to be smaller than desktop (“basket size on mobile is 32% smaller than on desktop”), highlighting meaningful differences in user behavior across devices.
Furthermore, the Global Device Share for eCommerce Purchases reported that for B2C e-commerce in 2025, 51.4% of purchase value is attributable to mobile devices, while 48.7% is attributed to desktop devices. This finding aligns with the Ecommerce Italia 2025 Report, which highlights a similar split: “in 2024 worldwide, mobile purchases (51.4%) surpassed desktop purchases (48.7%).”
Taken together, these statistics clearly illustrate the role mobile and in-app interactions play across modern customer journeys. When weighted against these trends, the importance that app tracking can play in your analytics is crucial. As a result, getting app tracking right is critical to understanding performance across channels and devices.
Multiple SDKs can be used to track mobile data, such as Amplitude, Mixpanel, or Segment. In this article, however, we will focus specifically on the Firebase SDK.
Firebase, Google’s comprehensive mobile development platform, offers powerful tools for collecting, analyzing, and acting upon app usage data for both Android and iOS applications. This article outlines best practices for implementing app tracking with Firebase, with a focus on data quality, organization, and long-term usability for analytics and business intelligence.
The Importance of Clean Data
The value of app tracking lies not just in collecting data, but in collecting clean, organized, and usable data. Poorly structured or inconsistent data can lead to inaccurate insights, flawed business decisions, and wasted resources.
Analytics teams, marketers, CEOs, managers, and data scientists all rely on a robust and reliable data foundation. That foundation supports everything from dashboards and reporting to pattern detection, behavioral analysis, and experience personalization.
There is something for everyone. However, the data must be good enough to be trusted and actionable.
Key Principles for Data Collection
High-quality app tracking starts with a clear set of data collection principles:
Accuracy: Ensure the data captured accurately reflects user actions and app states.
Completeness: Avoid missing data points that are critical for analysis.
Consistency: Maintain uniform naming conventions, data types, and structures across all events and properties.
Timeliness: Data should be collected and made available for analysis in a timely manner.
Relevance: Only collect data that directly contributes to answering specific business questions.
Event Structure: Best Practices
Everything starts somewhere.
If you are familiar with web-based tracking, you are most likely well-versed in data layers. When it comes to tracking data on websites, a well-formed and robust data layer can be critical to your tracking setup.
App tracking works differently. Whereas web analytics relies on a centralized dataLayer object that can persist information across multiple events on a page, Firebase app tracking treats each event as independent and transient.
With app tracking and the Firebase SDK, each event is triggered directly by the app using logEvent calls and sent immediately to the collection endpoint. There is no shared, persistent object that future events can reference.
This architectural difference makes event design, naming, and governance even more important in mobile app tracking.
Event Naming Conventions
Employ a clear and consistent naming convention for all events. This improves readability, governance, and downstream analysis.
Best practices include:
Lowercase with underscores: screen_view, product_added_to_cart
Action-object approach: view_item, add_to_cart, purchase
Prefixes for categories: ecommerce_purchase, engagement_session_start
Google provides extensive documentation on event naming conventions, including reserved event names and reserved prefixes. We strongly recommend reviewing this documentation, as the list is extensive and using reserved names can cause reporting issues.
If you already have a dataLayer present on your website, this is an excellent starting point for defining app event names. Do not try to reinvent the wheel. Using consistent naming across web and app helps ensure GA4 interprets events correctly.
For example, menu_interaction and menu_interaction_app would be treated as two distinct events in GA4, even if they represent the same user action in the same location.
Quick Tip: For this example, we recommend using the event name menu_interaction and relying on GA4’s default device dimensions to distinguish between web and app traffic.
Event Parameters
Each event should include relevant parameters that provide additional context. Parameters enable deeper analysis and more meaningful segmentation.
Standard parameters: Leverage Firebase’s recommended standard events and parameters, such as item_id, item_name, and value for e-commerce events.
Custom parameters: Define custom parameters for app-specific data points. Ensure they are consistently named and that their data types are clearly understood.
As with event names, avoid reinventing parameters when standard options already exist. Google provides multiple default parameters that can be reused across implementations. This reduces the need for custom definitions in GA4 and helps keep governance manageable, particularly for standard GA4 properties.
User Properties
User properties allow you to segment your audience and understand the characteristics of different user groups over time.
Examples include:
Demographic data: user_age_group, user_gender
App-specific traits: premium_user, subscription_status
Behavioral segments: last_app_version, days_since_last_purchase
When used consistently, user properties enable more meaningful audience analysis and long-term behavioral insights.
Building a Scalable Foundation for App Analytics
This is just the tip of the iceberg.
App tracking goes far beyond event names and parameters. However, getting these fundamentals right establishes the data foundation everything else depends on, from reporting and attribution to audience modeling and predictive analysis. Without clean, consistent app data, even the most advanced analytics strategies will fall short.
At Napkyn, we work with clients across industries to design, implement, and validate app tracking strategies using Firebase and GA4. In many cases, we see teams struggle not because they lack data, but because their app data is fragmented, inconsistent, or misaligned with how GA4 and downstream tools actually work. Addressing these issues early allows teams to move faster, trust their data, and unlock more advanced use cases over time.
Stay tuned for Part II, where we will cover common pitfalls in app tracking, how to make app data truly useful, how to define meaningful key performance indicators, and how to turn mobile data into actionable insights.
If you are reviewing your current app tracking setup or planning a new implementation, Napkyn can help you assess your data quality, align tracking with business goals, and ensure your Firebase and GA4 configuration is built to scale.
More Insights
Google Analytics: Mobile App Tracking with Firebase
Ricardo Cristofolini
Senior Implementation Specialist, Data Solutions
Jan 21, 2026
Read More
Google Tag Manager Environments: How to Set Them Up and Use Them Safely
Ricardo Cristofolini
Senior Implementation Specialist, Data Solutions
Jan 14, 2026
Read More
GA4 App QA Guide: How to Validate Mobile App Tracking with Firebase
Shreya Banker
Data Scientist
Jan 7, 2026
Read More
More Insights
Sign Up For Our Newsletter